nanotopo - Flux LoRA Model

A personalized Flux-based LoRA fine-tuned model trained on synthetically generated data from a single selfie, demonstrating how creative data augmentation can unlock diverse image generation capabilities.

Overview

This model began from a single selfie and evolved into a versatile Flux-LoRA model capable of generating a wide range of images featuring the trained subject in various styles, poses, and scenarios. The training leveraged the Hailuo Image-1 model’s subject-reference feature to generate 15 diverse training images from one source photo, showcasing an innovative approach to data augmentation for personalized AI models.

Trigger word: nanotopo
Base model: Flux.1 (dev/schnell)
Model type: LoRA fine-tune
Hardware: Nvidia H100 GPU

How It Works

Training Process

1. Source Material: Started with a single phone selfie
2. Data Augmentation: Used Hailuo Image-1’s subject-reference feature to generate 15 diverse training images
3. Image Specifications:
4. Resolution: 1024x1024 pixels
5. Variety: Different styles, poses, and situations
6. Consistency: Minimal, sober backgrounds with controlled lighting effects
7. Training Setup:
8. Prepared individual captions for each image
9. Compressed all images and captions into a ZIP file
10. Fine-tuned using replicate/fast-flux-trainer:56cb4a64
11. Training parameters optimized for portrait consistency

Key Features

• Single-source training: Demonstrates effective data augmentation from minimal input
• Versatile output: Generates consistent subject representation across diverse scenarios
• Style flexibility: Works with various artistic styles and compositions
• Fast inference: Supports both dev (28 steps) and schnell (4 steps) modes
• Optimization options: FP8 quantization available for faster generation

Usage

Basic Usage

To use this model, include the trigger word nanotopo in your prompt:

prompt = "nanotopo posing with an honest facial expression of satisfaction"

Recommended Settings

For best quality (dev model): - Model: dev - Inference steps: 28 - Guidance scale: 3.0 - LoRA scale: 1.0 - Aspect ratio: 1:1 or 16:9

For fast generation (schnell model): - Model: schnell - Inference steps: 4 - LoRA scale: 1.5 (automatically adjusted with go_fast mode) - Enable go_fast for FP8 quantization

Advanced Features

Image-to-Image: - Provide an input image to guide generation - Adjust prompt_strength (0-1) to control transformation intensity - Higher values = more deviation from source image

Inpainting: - Supply both an image and mask to regenerate specific regions - Useful for targeted edits while preserving the rest of the image

LoRA Stacking: - Use extra_lora parameter to load additional LoRA models - Combine multiple styles or concepts - Adjust extra_lora_scale independently

Parameter Guide

Common Use Cases

• Portrait generation: Create diverse portraits in different settings
• Style exploration: Apply various artistic styles while maintaining subject consistency
• Character consistency: Generate the same person across multiple scenarios
• Creative compositions: Place subject in imaginative or realistic scenarios
• Reference imagery: Create visual references for creative projects

Tips for Best Results

1. Always include the trigger word (nanotopo) for best subject activation
2. Start with guidance scale 2.5-3.5 for realistic images
3. Use dev model with 28 steps for highest quality
4. Use schnell with 4 steps + go_fast for rapid iteration
5. Experiment with LoRA scale between 0.8-1.2 for different intensities
6. Keep prompts descriptive but not overly complex
7. Specify lighting and composition for more controlled results

Limitations

• Subject specificity: Trained on a single individual; not suitable for other subjects
• Dataset scope: Limited training images may restrict pose/angle variety
• Style transfer: Some artistic styles may work better than others depending on training data
• Resolution: Optimal at 1024x1024; custom dimensions may affect quality
• Coherence: Complex scenes with multiple subjects may reduce consistency

Troubleshooting

Subject not appearing correctly: - Ensure trigger word nanotopo is in the prompt - Increase lora_scale to 1.2-1.5 - Try higher guidance_scale (3.5-4.0)

Images look overcooked or artificial: - Reduce guidance_scale to 2.0-2.5 - Lower lora_scale to 0.7-0.9 - Increase inference steps if using schnell

Generation too slow: - Enable go_fast mode - Switch to schnell model with 4 steps - Reduce num_outputs to 1

Technical Details

Training approach: Dreambooth-style LoRA fine-tuning
Rank: 16 (estimated)
Training images: 15 synthetically generated variations
Source diversity: Multiple styles, poses, and lighting conditions
Trainer: replicate/fast-flux-trainer:56cb4a64

Ethical Considerations

This model was trained on self-generated images with explicit consent from the subject. Users should: - Respect privacy and consent when using personalized models - Avoid generating content that misrepresents or harms individuals - Follow platform guidelines and local regulations regarding AI-generated imagery - Consider watermarking or disclosing AI-generated content where appropriate

Links & Resources

• Model weights: HuggingFace
• Training method: Hailuo Image-1 subject-reference feature
• Base trainer: replicate/fast-flux-trainer
• Related models: topolora1

Citation

If you use this model or methodology in your work, please reference:

nanotopo - Flux LoRA Model
Creator: topogoogles
Platform: Replicate
URL: https://replicate.com/topogoogles/nanotopo
Training approach: Single-source synthetic data augmentation via Hailuo Image-1

Version History

• Current version: Initial release (3 months, 2 weeks ago)
• Status: Warm model (reduced cold boot times)
• Run count: 13+ successful generations

Questions or issues? Feel free to reach out through the Replicate platform.